Method and system for licensing by location

ABSTRACT

An agricultural input supplier can provide seed products by tying the compensation received for the seed products to an evaluation of the land base on which the seed products are to be planted and/or the performance of the seed products on the land base. The evaluation of the land base may be based on environmental classification and/or genotype-by-environment data. In addition to tying the compensation to the land quality and the seed performance, this approach to providing seed allows for recommendations to be made to the producer regarding which seed products should be used on the land base. The performance of the seed product on the land base may also be verified.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119 of a provisionalapplication Ser. No. 60/689,716 filed Jun. 10, 2005, which applicationis hereby incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

The present invention provides for computer-implemented methods andrelated methods which apply a performance-based pricing model to theselling of seed products

The productivity of ground can vary significantly, from fertile groundwith a desirable actual production history (APH) to lower quality groundwith a poor performance history. Producers planting on less productiveground have a greater need to keep the cost of agricultural inputs, suchas seed, as low as possible. Thus, a producer may be weary ofgenetically superior seed products which, even though would increaseperformance over alternatives, have an associated premium price. Where aproducer is using less productive ground they simply may not be able tojustify the premium price.

What is needed therefore is a method for licensing and/or selling seedto the producer where the price of the seed is based at least in part onthe location and quality of the land on which the seed is to be planted.Further needed is a method of auditing the performance of the seedproduct on the land base during and after planting of the seed product.

SUMMARY OF THE INVENTION

Therefore it is a primary object, feature, or advantage of the presentinvention to improve over the state of the art.

Another object, feature or advantage of the present invention is toprovide a method to license and/or sell seed to producers where theprice of the seed is based at least in part on the quality of the landon which the seed is to be planted.

Yet another object, feature or advantage of the present invention is toprovide a method to license and/or sell seed to producers where theprice of the seed is based at least in part on the expected performanceof the seed product.

A further objective, feature, or advantage of the present invention isto assist producers in selecting the best seed product for a particularlocation.

A still further objective, feature, or advantage of the presentinvention is to verify that seed is used in the location where it isreported to be used.

Another objective, feature, or advantage of the present invention is toaudit the performance of the seed product during and after the seedproduct has been planted on the land base.

Yet another object, feature, or advantage of the present invention is toprovide additional incentives to producers for selecting seeds productsfor a particular location with a greater likelihood of performance.

One or more of these and/or other objects, features, or advantages ofthe present invention will become apparent from the specification andclaims that follow.

According to one aspect of the present invention a method for sellingseed products for planting by a crop producer is provided. The methodincludes characterizing the land base where the seed will be planted,determining the type of seed to be planted, and pricing the seedproduct. The seed product is priced based at least in part onperformance or expected performance of the seed product within the landbase. To assist in determining expected performance, the land may becharacterized using environment classification systems.

A recommendation or requirement for the type of seed used may beprovided. The type of seed planted at the land base may be determined atleast partially based on performance of the seed product in anenvironmental classification associated with the land. The seed productmay be recommended at least partially based on expectedgenotype-by-environment interactions between the seed product and theland base.

According to another aspect of the present invention, a method ofselling seed products to a producer ties compensation for the seedproducts to the quality of the land base of the producer. The methodincludes evaluating the land base of the producer to determine a qualityof the land base. In addition to the quality of the land base,determining the compensation for the seed product may, withoutlimitation, be at least partially based on the expected performance ofthe seed product within the land base and/or the actual performance ofthe seed product within the land base.

Evaluation of the land base may be performed by using an environmentalclassification of the land base. The environmental classification may beselected from a set of environmental classes. For example, theenvironmental classes can be a temperate class, a temperate dry class, atemperate humid class, a high latitude class, s subtropical class orbiotic classification.

The performance of the at least one seed product within the land basemay also be audited. Particularly, where pricing is based on actualperformance, there is a need to audit performance to ensure that a fairprice is arrived upon. The auditing may be done by several differentmeans, including, but not limited to, auditing GPS data (including, butnot limited to, GPS data associated with planting and/or harvesting seedproduct in the land base) associated with crop production in the landbase, reviewing weigh tickets and/or yield monitoring data for cropsproduced from the land base, and/or remote sensing of the land base.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flow diagram illustrating one process for determininggenotype-by-environment interactions and using that information incategorizing land bases into different environmental classifications.

FIG. 2A to FIG. 2C provide an example of genotype by environmentinteractions and cross-over interactions between two different varietiesin four different environmental classes.

FIG. 3 illustrates environment-standardized GGE biplot of grain yield of18 maize hybrids (H1-H18) grown in 266 environments over three yearsstratified by state.

FIG. 4 illustrates environment-standardized GGE biplot of grain yield of18 maize hybrids (H1-H18) grown in 266 environments over three yearsstratified by environmental class.

FIG. 5 illustrates one possible schematic for categorizing differentland bases into environmental classifications based on temperatures,solar radiation, and length of photoperiod.

FIG. 6 is a bar graph representation of the frequency of variousenvironmental classes among target population of environments (TPEs) ormulti-environment trials (METs).

FIG. 7 illustrates potential categories of environmental classesidentified throughout the United States in 1988 and their locations;these include temperate, temperate dry, temperate humid, high latitude,and subtropical classes.

FIG. 8 is a flow diagram illustrating information flow from anenvironmental profile and a producer profile to providingrecommendations to a producer according to one embodiment of the presentinvention.

FIG. 9 is block diagram illustrating a system for determining productrecommendations according to one embodiment of the present invention.

FIG. 10 is a screen display according to one embodiment of the presentinvention.

FIG. 11 is a screen display showing a product portfolio according to oneembodiment of the present invention.

FIG. 12 is a screen display for one embodiment of an application of thepresent invention.

FIG. 13 is a screen display for one embodiment of an application of thepresent invention.

FIG. 14 is a screen display for one embodiment of the present inventionshowing field-by-field product recommendations.

FIG. 15 is a flow diagram for one embodiment of a sales tool fordemonstrating the value of environmental classification in describingrelative performance.

FIG. 16 is a screen display illustrating one example of output from asales tool of the present invention for demonstrating the value ofenvironmental classification in describing relative performance.

FIG. 17 is a flow diagram showing information flow in a productselection and positioning application of the present invention.

FIG. 18 is a block diagram illustrating formation of a license forplanting seed product in a particular location and auditing the plantingof the seed product in the location.

FIG. 19 is a flow diagram illustrating information flow in formation ofa license for planting seed product in a particular location andauditing the planting of the seed product in the location.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present invention provides for computer-implemented methods andrelated methods which tie compensation received for seed products toactual performance of the seed product within a particular field (orother land base) or to expected performance of the seed product within aparticular field or other land base. The present invention may useenvironmental classification and/or an understanding ofgenotype-by-environment interactions to evaluate the land base and/ordetermine the compensation for the seed product. Thus, seed can berecommended to, sold to, and/or provided to a producer where thecompensation for or price of the seed is determined based uponperformance-expected performance or actual performance.

Where the performance is actual performance, it may be audited in orderto, among other things, verify the use of the seed within the particularland base. In other words, if compensation is tied to performance of aparticular field, some form of auditing is needed to verify that theseed is actually planted in the field which it was purchased for. Thistype of verification is desirable both when the pricing is based onactual performance and expected performance. A second level type ofverification is used to verify the performance of the crop. Verifyingthe use of the seed within the particular land base can be performed byevaluating various data typically associated with crop planting,production and harvesting.

Where the compensation provided to an agricultural input supplier (suchas a reed company) is tied to performance, the agricultural inputsupplier has an added incentive for the producer to be successful. Oneway in which the agricultural input supplier can potentially increasethe likelihood of the producer being successful is by making appropriaterecommendations of the agricultural inputs to be used, particularly bywhere the agricultural input supplier is in a better position than theproducer to make such a decision. A producer will understand that theagricultural input supplier is making recommendations to increaseperformance and may therefore be more willing to that and reply upon therecommendation. In addition, the agricultural input supplier may requirethe producer to take their recommendations in order to reduce the riskcreated for it.

One method to tie compensation received for seed products to anevaluation of the land base on which the seed products are or will beplanted uses knowledge of genotype-by-environment interactions to assista producer or other customer in selecting seed products to plant in oneor more fields. A “genotype” is generally defined as a cultivar,genetically homogenous (lines, clones), a hybrid of two or more parents,or heterogeneous (open-pollinated populations). An “environment” isgenerally defined as a set of conditions, such as climatic conditions,soil conditions, biotic factors (such as, without limitation, pests anddiseases) and/or other conditions that impact genotype productivity.“Management” as used in this context generally refers to productionmanagement decisions, such as, but not limited to crop productionpractices. In addition, the present invention allows for the use ofenvironmental characterizations to assist in describinggenotype-by-environment interactions. It is to be understood that theterm “genotype-by-environment” (G×E) is to encompass what is sometimesknown or referred to as “genotype-by-environment-by management” (G×E×M)as the environment associated with a plant may include managementpractices which affect the environment (for example, irrigation may beconsidered a management practice, but use of irrigation affects thegrowing environment).

Following, is an exemplary description regarding the use of G×Einteractions and environmental classification. Next, the determinationof compensation in exchange for seed products where the compensation isat least partially based on evaluating the land is discussed. Then, theverification of the performance of the seed and other means by whichrisk can be minimized is discussed.

G×E and Environmental Classification

Genetic manipulation alone does not ensure that a plant will performwell in a specific environment or for that matter a wide range ofenvironments year after year. In other words, there is no one genotypethat is likely to performance best in all environments or under allmanagement practices. The performance or phenotype results from aninteraction between the plant's genotype and the environment and themanagement practices used.

It is to be understood that there are some inherent difficulties inunderstanding such interactions. An environment at a given locationchanges over the years making multi-environment trials (METs) performedin the same location limited as to inferences about future cropperformance. Furthermore, inferences about a crop's future performancein different locations depend on whether the target population ofenvironments (TPEs) is well sampled since the environment varies betweendifferent locations in one year.

To assist in analyzing such interactions, the present inventionpreferably uses environmental classification techniques. Theenvironmental classification techniques are used, preferably with alarge set of data to relate performance of different genotypes todifferent environments. Environmental classification is then used whenselecting the best seed products for a particular land base. Thus, forexample, a producer can use environmental classification to select thebest seed products for their land base based on the expectedenvironmental conditions. Alternatively, the producer may diversify andselect a combination of seed products based on a range of expectedenvironmental conditions to thereby manage risks associated withenvironmental variability. Of course, environmental classification canbe used by not just producers but others having interest in agriculturalproduction.

FIG. 1 provides an overview of one G×E paradigm where G×E knowledge 12is used in planning and positioning 18. G×E knowledge 12 can be appliedto crop modeling 14. Crop modeling 14 and G×E knowledge 12 may eitheralone or together be used to classify environments. The G×E knowledge 12and classified environments may be used in facilitating the positioningand/or planning 18 strategies, such as characterization of products,resource efficiency, risk management, product positions, and productselection.

Subsequent to positioning and planning, the producer will grow theselected products 26 and measure the performance results 24. Theproducer may also collect environmental and physiological landmark data28 and in conjunction with performance results 24 use it in analysis 20.Analysis of environmental and physiological landmark data 28 andperformance results 24 may undergo analysis 20 using G×E analysis toolsor period-of-years database 22.

Building an Environmental Classification System

The effectiveness of a product evaluation system for genotypeperformance largely depends on the genetic correlation betweenmulti-environment trials (MET) and the target population of environments(TPE) (Comstock, R. E. 1977. ‘Proceedings of the InternationalConference on Quantitative Genetics, Aug. 16-21, 1976’ pp. 705-18. IowaState University Press. Ames, USA.). For example, previouscharacterizations of maize environments relied mainly on climatic andsoil data (e.g. Hartkamp, A. D., J. W. White, A. Rodriguez Aguilar, M.Bänziger, G. Srinivasan, G. Granados, and J. Crossa. 2000. MaizeProduction Environments Revisited: A GIS-based Approach. Mexico,D.F.CIMMYT.; Pollak, L. M., and J. D. Corbett. 1993. Agron. J.85:1133-1139; Runge, E. C. A. 1968. Agron. J. 60:503-507.). While usefulto describe environmental variables affecting crop productivity, theseefforts did not quantify the impact of these variables on the geneticcorrelations among testing sites. Consequently, plant breeders have moreextensively used characterizations of environments based on similarityof product discrimination in product evaluation trials (e.g. Cooper, M.,D. E. Byth, and I. H. DeLacy. 1993. Field Crops Res. 35:63-74.).However, these efforts frequently fail to provide a long-term assessmentof the target population of environments (TPE), mainly due to the costand impracticality of collecting empirical performance data forwidespread and long-term studies.

The present invention provides a modern approach of product evaluationwhere a TPE is described. The description of a TPE includes classifyingthe land base into an environmental class and assessing the frequency ofoccurrence of the range of environments experienced at a given location.The present inventors contemplate that areas of adaption (AOA) couldalso be evaluated. As used herein AOA refers to a location with theenvironmental conditions that would be well suited for a crop orspecific genotype. Area of adaption is based on a number of factors,including, but not limited to, days to maturity, insect resistance,disease resistance, and drought resistance. Area of adaptability doesnot indicate that the crop will grow in every location or every growingseason within the area of adaption or that it will not grow outside thearea. Rather it defines a generally higher probability of success for acrop or genotype within as opposed to outside that area of adaptation.

The environmental information collected may be used to develop anenvironmental database for research locations. Initially, multipleenvironment trials are performed by planting different genotypesavailable from a variety of sources, e.g. germplasm, inbreds, hybrids,varieties in multiple environments. These trials aid the determinationof whether the TPEs are homogenous or should be categorized intodifferent environmental classifications. The performance data of thesegenotypes and environmental and/or physiological landmark data from theMET are collected and entered into a data set. For example, performancedata collected for a genotype of corn may include any of the following:yield, grain moisture, stalk lodging, stand establishment, emergence,midsilk, test weight, protein, oil, and starch. Yield refers to bushelsof grain per acre. Grain moisture refers to a moisture determinationmade from each plot at harvest time, using an instrument such as anelectrical conductance moisture meter. Stalk lodging refers to thedetermination of the number of broken stalks in each plot prior toharvest. Stand establishment refers to the differences between thedesired planting rate for each hybrid and the final stand. Emergencerefers to an emergence count made on each plot after plant emergencewhere emergence percentage may be computed based on the number of plantsand the number of kernels planted. The mid silk date is the Julian dayof the year in which 50% of the plants show silks at one site in aregion. The test weights are typically reported as pounds per bushel ongrain samples at field moisture. Protein, oil and starch are typicallyreported as a percent protein, oil, and starch content at a designatedpercent grain moisture on dried samples using standard methods, forexample, a near infrared transmittance whole grain analyzer.

One skilled in the art would be familiar with performance data collectedfor other crops, for example, soybeans, wheat, sunflowers, canola, riceand cotton. Performance data for soybeans include, without limitation,relative maturity, plant height, lodging score, seed size, protein andoil percentage, Phytophthora resistance genes, Phytophthora partialresistance, Sclerotinia rating, and yield. Relative maturity refers to adetermination that is designed to account for factors, such as soybeanvariety, planting date, weather, latitude and disease that affectmaturity date and number of days from planting to maturity. Plant heightrefers to a determination of the soybean plant's height, usuallydetermined prior to harvest. Lodging, traditionally, the verticalorientation of the plant, i.e. the degree to which the plant is erect.The lodging of a soybean plant is traditionally rated by researchersusing a scale of 1 to 9 as follows: 1.0=almost all plants erect,3.0=either all plants leaning slightly, or a few plants down, 5.0=eitherall plants leaning moderately (45O angle), or 25-50% down, 7.0=eitherall plants leaning considerably, or 50-80% down, 9.0=all plantsprostrate. The seed size of a soybean plant typically refers tothousands of seeds per pound. Protein and oil percentage analysis may bedetermined using near infrared transmittance technology and reported at13% moisture. Phytophthora resistance genes may be determined using ahypocotyl inoculation test with several races of Phytophthora todetermine the presence or absence of a particular Rps gene in a soybeanplant. Soybeans may also be evaluated for phytophthora partialresistance using a ratings system, where ratings of 3.0 to 3.9 areconsidered high levels of partial resistance, ratings of 4.0 to 5.9 areconsidered moderate, ratings over 6.0 indicate very little partialresistance or protection against Phytophthora. Soybeans may also beevaluated for partial resistance to Sclerotinia. Yield refers to bushelsper acre at 13 percent moisture.

Typical performance data for wheat includes, without limitation, testweight, protein percent, seed size, percent lodging, plant height,heading date, powdery mildew, leaf blotch complex (LBC), Fusarium headscab (FHS), flour yield, and flour softness. Test weight refers to adetermination of pounds/bushell using harvest grain moisture. Seed sizerefers to thousands of harvested seeds per pound. Percent lodging asdescribed previously refers to a rating system used to estimate thepercent of plants that are not erect or lean more than 45 degrees fromvertical. Plant height refers to the distance from the soil surface tothe top of the heads. Heading date refers to the average calendar day ofthe year on which 50 percent of the heads are completely emerged. Wheatinfected with powdery mildew (PM) may be determined using a scale systemwhere each plot is rated based on a 0 to 10 scale where: 0=0 to trace %leaf area covered; 1=leaf 4 with trace—50%; 2=leaf 3 with 1-5%; 3=leaf 3with 5-15%; 4=leaf 3 with >15%; 5=leaf 2 with 1-5%; 6=leaf 2 with 5-15%;7=leaf 2 with >15%; 8=leaf 1 with 1-5%; 9=leaf 1 with 5-15%; and 10=leaf1 with >15% leaf area covered (leaf 1=flag leaf). This scale takes intoaccount the percentage leaf area affected and the progress of thedisease upward on the plants. Leaf blotch complex (LBC) caused byStagonospora nodorum, Pyrenophora triticirepentis and Bipolarissorokiniana for example may be determined when most varieties are in thesoft dough growth stage and rated based on the percentage of flag leafarea covered by leaf blotches. Fusarium head scab (FHS) caused byFusarium graminearum for example may be determined when plants are inthe late milk to soft dough growth stage and each plot is rated based ona disease severity estimate as the average percentage of spikeletsaffected per head. Flour yield refers to the percent flour yield frommilled whole grain. Flour softness refers to the percent offine-granular milled flour. Values higher than approximately 50 indicatekernel textures that are appropriate for soft wheat. Generally, highvalues are more desirable for milling and baking.

Typical performance data for sunflower includes, without limitation,resistance to aphids, neck breakage, brittle snap, stalk breakage,resistance to downy mildew (Plasmopara halstedii), height of the head atharvest, seed moisture, head shape, hullability, resistance to thesunflower midge, Contarinia schulzi, percentage of oil content, seedsize, yield, seedling vigor, and test weight. Resistance to aphidsrefers to a visual ratings system indicating resistance to aphids basedon a scale of 1-9 where higher scores indicate higher levels ofresistance. Neck breakage refers a visual ratings system indicating thelevel of neck breakage, typically on a scale from 1 to 9 where thehigher the score signifies that less breakage occurs. Brittle snaprefers to a visual rating system indicating the amount of brittle snap(stalk breakage) that typically occurs in the early season due to highwinds. The ratings system is based on a scale, usually ranging from 1-9,with a higher score denoting the occurrence of less breakage. Asunflower's resistance to Downy Mildew (Plasmopara halstedii) may bedetermined using a visual ratings scaled system with 9 being the highestand 1 the lowest. A higher score indicates greater resistance. Height ofthe head at harvest refers to the height of the head at harvest,measured in decimeters. Seed moisture refers to a determination of seedmoisture taken at harvest time, usually measured as a percentage ofmoisture to seed weight. Head shape of a sunflower is measured visuallyusing a scale system where each plot is rated based on a 1 to 9 scalewhere: 1=closed “midge” ball; 2=trumpet; 3=clam; 4=concave; 5=cone;6=reflex; 7=distorted; 8=convex; 9=flat. Hullability refers to theability of a hulling machine to remove seed hulls from the kernel,typically measured on a 1-9 scale where a higher score reflects betterhullability. Resistance to the sunflower midge, Contarinia schulzi, isdetermined based on head deformation which is rated on a 1-9 scalewhere: 9=no head deformation (fully resistant), 5=moderate headdeformation, 1=severe head deformation (fully susceptible). Thepercentage of oil content from the harvested grain is measured andadjusted to a 10% moisture level. The oil content of a sunflower seedmay be measured for various components, including palmitic acid, stearicacid, oleic acid, and linoleic acid, using a gas chromatograph. Seedsize refers to the percentage of grain that passes over a certain sizescreen, usually “size 13.” Seedling vigor refers to the early growth ofa seedling and is often times measured via a visual ratings system, from1-9, with higher scores indicate more seedling vigor. Yield is measuredas quintals per hectare, while test weight of seed is measured askilograms per hectoliter.

Typical performance data for canola includes, without limitation, yield,oil content, beginning bloom date, maturity date, plant height, lodging,seed shatter, winter survival, and disease resistance. Yield refers topounds per acre at 8.5% moisture. Oil content is a determination of thetypical percentage by weight oil present in the mature whole driedseeds. Beginning bloom date refers to the date at which at least oneflower is on the plant. If a flower is showing on half the plants, thencanola field is in 50% bloom. Maturity date refers to the number of daysobserved from planting to maturity, with maturity referring to the plantstage when pods with seed color change, occurring from green to brown orblack, on the bottom third of the pod bearing area of the main stem.Plant height refers to the overall plant height at the end of flowering.The concept of measuring lodging using a scale of 1 (weak) to 9 (strong)is as previously described. Seed shatter refers to a resistance tosilique shattering at canola seed maturity and is expressed on a scaleof 1 (poor) to 9 (excellent). Winter survival refers to the ability towithstand winter temperatures at a typical growing area. Winter survivalis evaluated and is expressed on a scale of 1 to 5, with 1 being poorand 5 being excellent. Disease resistance is evaluated and expressed ona scale of 0 to 5 where: 0=highly resistant, 5=highly susceptible. TheWestern Canadian Canola/Rapeseed Recommending Committee (WCC/RRC)blackleg classification is based on percent severity index described asfollows: 0-30%=Resistant, 30%-50%=Moderately Resistant,50%-70%=Moderately Susceptible, 70%-90%=Susceptible, and >90%=Highlysusceptible.

Typical performance data for cotton includes, without limitation, yield,turnout, micronaire, length, fiber strength of cotton and color grade.Yield is measured as pounds per acre. Turnout refers to lint and seedturnout which is calculated as the percentage of lint and seed on aweight basis as a result of ginning the sub sample from each treatment.Micronaire refers to fiber fineness and maturity and are measured usingair flow instrument tests in terms of micronaire readings in accordancewith established procedures. Fiber length is reported in 1/32 of an inchor decimal equivalents. Fiber strength is measured in grams per tex andrepresents the force in grams to break a bundle of fibers one tex unitin size. Color grade for cotton takes into consideration the color,fiber color and whiteness of cotton leaves. Color grade may bedetermined using a two digit scale. The two digit number is anindication of the fiber color and whiteness (i.e. 13, 51, or 84). Thefirst digit can range from 1 to 8 representing overall color with 1being the best color and 8 representing below grade colors. The seconddigit represent a fiber whiteness score. This number ranges from 1 to 5,with 1 representing good white color and 5 representing yellow stained.The second number in the overall color grade represents the leaf scoreand represents leaf content in the sample.

Typical performance data for rice includes, without limitation, yield,straw strength, 50% Heading, plant height, and total milling, and totalmilling. Yield is measured as bushels per acre at 12% moisture. StrawStrength refers to lodging resistance at maturity and is measured usinga numerical rating from 1 to 9 where 1=Strong (no lodging); 3=Moderatelystrong (most plants leaning but no lodging); 5=Intermediate (most plantsmoderately lodged); 7=Weak (most plants nearly flat); and 9=Very weak(all plants flat). 50% heading refers to the number of days fromemergence until 50% of the panicles are visibly emerged from the boot.Plant height is the average distance from the soil surface to the tip oferect panicle. Total milling refers to the total milled rice as apercentage of rough rice. Whole milling refers to rice grains of ¾length or more expressed as a percentage of rough rice.

The environmental and physiological landmark data may be historicalusing historical meteorological information along with soils and otheragronomic information or collected using National Oceanic andAtmospheric Association and/or other public or private sources ofweather and soil data. Potential environmental and physiologicallandmark data that may be collected includes but is not limited to wind,drought, temperature, solar radiation, precipitation, soil type, soilpH, planting and harvesting dates, irrigation, tiled area, previouscrop, fertilizer including nitrogen, phosphorous, and potassium levels,insecticide, herbicide, and biotic data, for example, insects anddisease. The environmental and physiological landmark data may then beanalyzed in light of genotype performance data to determine G×Einteractions.

Models

Several models for determining G×E interactions exist. Base models groupor classify the locations used to test the hybrids, include severalvariance components, and stratify the hybrids, for example, according tolocations among station-year combinations, locations, or other chosenvariances.

For example, as shown in Table 1, one base model Year Station (YS)groups the locations by year-stations where a year-station designates aunique site or location by year. Other variances include blocks withinlocations within year-stations, hybrids, hybrids by year-station dividedby the sum of hybrids by locations within year station locations as wellas a residual. The YS model is disadvantageous in that a givenlocation's environment will vary over time so that the G×E informationgleaned from the model may not be relevant for predicting hybrids thatwill perform well in the same location next year.

Another model for determining G×E interactions disclosed in Table 1,groups different sites by location. Other variances for the G×E modelinclude blocks within locations, hybrids, hybrids by locations, as wellas a residual. However, the G×E model is disadvantageous in that agenotype grown in locations with differing environmental conditions mayhave similar performance results, complicating the analysis of thespecific environmental conditions that play a role in contributing togenotype performance and reducing the certainty of predicting productperformance.

Unlike the previous models mentioned, the present inventors contemplatedetermining G×E interactions using a model referred to herein asEnvironmental Classification that groups locations by environmentalclassifications. Thus, variances for this model include locations withinenvironmental classifications, blocks within locations withinenvironmental classifications, hybrids, hybrids by environmentalclassifications divided by hybrids by locations within environmentalclassifications and a residual. TABLE 1 Models for determining G × Einteractions Environmental Model Year-Station G × E ClassificationVariance for Location within Location Location within locationyear-station environmental classification Variance for blocks withinblocks within blocks within location locations locations locationswithin within year- environmental station classifications Variance forhybrids hybrids hybrids hybrids Stratifications hybrid by hybrid byhybrid by year-station/ locations environmental hybrids byclassifications/ locations hybrid by within locations locations withinenvironmental classifications

Burdon has shown that genetic correlation between G×E interactions canbe estimated. (Burdon, R. D. 1977. Silvae Genet., 26: 168-175.). G×Eanalysis may be performed in numerous ways. G×E interactions may beanalyzed qualitatively, e.g. phenotype plasticity, or quantitativelyusing, for example, an analysis of variance approach. (Schlichting, C.D. 1986. Annual Review of Ecology and Systematics 17: 667-693.).Statistical analysis of whether a G×E interaction is significant andwhether environmental changes influence certain traits, such as yieldperformance, of the genotypes evaluated may be performed using anynumber of statistical methods including but not limited to, rankcorrelation, analysis of variances, and stability.

Rank Correlation

The most basic categorization of G×E interaction is to evaluate G×Einteractions by performing a rank correlation according to standardizedtests, for example, Spearman. The Spearman rank correlation may beperformed to examine the relationships among genotypes in differentenvironments, for example, crossover interactions that occur when twogenotypes change in rank order of performance when evaluated indifferent environments. FIG. 2 illustrates an example of G×Einteractions and cross-over interactions (COI) between two differentvarieties, Var A and Var B, in four different environmental classes, Env1, Env 2, Env 3 and Env 4. FIG. 2A shows that Var A and Var Bout-perform each other in different environments indicating theoccurrence of both G×E and COI. FIG. 2B shows that Var A performedbetter than Var B in each environment, indicating G×E interactions butno COI. In contrast, FIG. 2C shows that Var A and Var B each performedconsistently with respect to each other in all four environments,indicating lack of G×E interactions.

Analysis of Variance (ANOVA)

Alternately, G×E interactions may be analyzed using an analysis ofvariance method (ANOVA) (Steel, R. G. D and J. H. Torrie. 1980.Principles and Procedures of Statistics, 2nd edition) over environmentsto determine the significance of genotypes, environments and G×Einteractions. G×E interactions may also be analyzed using ASREML(Gilmour, A. R., Cullis, B. R., Welham, S. J. and Thompson, R. 2002ASReml Reference Manual 2nd edition, Release 1.0 NSW AgricultureBiometrical Bulletin 3, NSW Agriculture, Locked Bag, Orange, NSW 2800,Australia.) for the computation of variance components, and thegeneration of GGE biplots (Cooper, M., and I. H. DeLacy. 1994. Theor.Appl. Genet. 88:561-572; Yan, W. and M. S. Kang. 2003. GGE BiplotAnalysis: A Graphical Tool for Breeders Geneticists, and Agronomists.CRC Press. Boca Raton, Fla.). FIG. 3 and FIG. 4 illustrateenvironment-standardized GGE biplot of grain yield of 18 maize hybrids(H1-H18) grown in 266 environments over three years, stratified by stateor by environmental class respectively.

Stability

Once certain genotypes are identified that perform well in a targetenvironment they may be analyzed to determine which hybrids are morestable in yield or other metrics using various methods. One method usesa regression of genotypic performance on an environmental index. Ingeneral, the environmental index is the deviation of the mean phenotypeat environment from the overall mean phenotype of all environments.Thus, the phenotype of an individual genotype with each environment isregressed on the environmental index, as described in Bernardo R. 2002.Quantitative Traits in Plants. Stemma Press, Woodbury, Minn. to generatea slope (b-value) for each genotype/cultivar evaluated. Other methodsinclude the joint regression analysis method proposed by Perkins, J. M.and Jinks, J. L. 1968. Heredity. 23: 339-359, Finlay, K. W. andWilkinson, G. N. 1963. Aust. J. Res. 14: 742-754 and Eberhart, S. A. andRussell, W. A. 1966. Crop Sci. 6:36-40 to calculate the regressioncoefficient (b), S.E. and variance due to deviation from regression(S2d) as a parameter of stability and adaptability. The model describedby Eberhart and Russell has the following formula:P _(ij) =μ+g _(i) +b _(i) t _(j)+δ_(ij) +e _(ij)

-   -   where P_(ij) is the mean phenotype of genotype or cultivar i in        location j, μ is the grand mean across the whole experiment for        all genotypes and locations,    -   g_(i) is the effect of genotype i across all locations    -   b_(i) is the linear regression of P_(ij) on t_(j),    -   t_(j) is the environmental index, that is the effect of        environment j across all genotypes),    -   δ_(ij) is the deviation P_(ij) from the linear regression value        for a given t_(j) and    -   e_(ij) is the within environment error.        Categorization of Land Bases into Environmental Classes

Using the information collected for or from G×E analysis, the land basesmay be categorized into environmental classifications. FIG. 5illustrates one possible schematic for categorizing different land basesinto environmental classifications. With reference to FIG. 5, one methodof categorizing environmental classifications is illustrated as a flowchart. If all maximum temperatures are greater than 28° Celsius 42, thenthe land base may be categorized as either Temperate Dry 54, TemperateHumid 52, Temperate 56, or Subtropical 48. If all maximum temperaturesare greater to or equal to 30° Celsius and solar radiation is greaterthan 24 and 21 at a given crop development stage, e.g. v7-R1, R3-R6 40,then the land base is characterized as Temperate Dry 54. If the maximumtemperature is not greater than or equal to 30° Celsius and solarradiation is not greater than 24 at a given crop development stage, e.g.V7-R1 and 21 for R3-R6 respectively 40, then the land base ischaracterized as Temperate 56. However, if the maximum temperature isless than 30° Celsius and solar radiation is greater than 24 and 21 at agiven crop development stage 50, then the land base is characterized asTemperate Humid 52. If the maximum temperature is not less than 30°Celsius and solar radiation is not greater than 24 and 21 at a givencrop development stage 50, then the land base is characterized asTemperate 56. If all maximum temperatures 42 for the land base are lessthen 28° Celsius than the land base is characterized as High Latitude44. In contrast, if all maximum temperatures 42 for the land base arenot less then 28° Celsius and the land base has a photoperiod less than13.4 hours/day 46, then the land base is Subtropical 48.

Categorizing land bases into environmental classifications has severaladvantages. First, environmental classifications can bring anunderstanding of the various environments under which crops areproduced. Second, occurrence probabilities for each environmentalcategory can be assigned to each geographic location and the frequencyof the classifications determined using routine methods. FIG. 6 is a bargraph representation of the frequency of various environmental classesamong TPEs or METs. The frequency for each environmental class, e.g.temperate, temperate dry, temperte humid, high latitude, andsubtropical, is given as a percent of the total TPE or MET tested ingiven year or across years. FIG. 7 illustrates potential categories ofenvironmental classes identified throughout the United States in 1988and their locations; these include temperate, temperate dry, temperatehumid, high latitude, and subtropical classes. It will be apparent toone skilled in the art that other environmental classifications mayadded as identified or deemed relevant to G×E interactions for variouscrops.

Some of the environmental classification may be defined using generalcharacteristics of climates. For example, temperate may be used to referto regions in which the climate undergoes seasonal change in temperatureand moisture; typically these regions lie between the Tropic ofCapricorn and Antarctic circle in the Southern Hemisphere and betweenthe Tropic of Capricorn and the Arctic circle in the NorthernHemisphere. Temperate humid may refer to regions in which the climateundergoes seasonal change in temperature and moisture and has morehumidity than a temperate environment. High latitude as an environmentalclass may refer to regions that have a longer photoperiod than and istypically north of a particular latitude. A subtropical class may referto regions enjoying four distinct seasons usually with hot tropicalsummers and non-tropical winters with a shorter photoperiod/day length;typically these regions lie between the ranges 23.5-40° N and 23.5-40° Slatitude. The environmental classes may also be defined by bioticfactors, such as diseases, insects, and/or characteristic of a plant.For example, an ECB class may refer to regions having European CornBorers (ECB) or the suspected presence of ECB as evidenced bypreflowering leaf feeding, tunneling in the plant's stalk, postflowering degree of stalk breakage and/or other evidence of feeding. Theenvironmental class Brittle may be used to refers to regions where stalkbreakage of corn occurs or is apt to occur near the time of pollinationand is indicative of whether a hybrid or inbred would snap or break nearthe time of flowering under severe winds.

It is to be understood that the environmental classifications may beused and defined differently for different crops/genotypes and thatthese definitions may vary from year to year, even for the same crops orgenotypes. For example, in 2000-2003, trials conducted studying G×Einteractions among Comparative Relative Maturity (CRM) hybrids of CRM103-113 in different environments identified seven differentenvironmental classes—temperate, temperate dry, temperate humid, highlatitude, subtropical, ECB, and brittle. For the study purposes,temperate was identified/defined as having a low level of abioticstresses, a growing season adequate for CRM 103-113, and found to befrequent in Iowa and Illinois. Temperate dry was defined as temperatewith some level of water and/or temperature stress and found to befrequent in Nebraska, Kansas, and South Dakota. Temperate Humid wasdefined as similar to the temperate environmental class but had acomplex of biotic factors, such as leaf disease, that may differentiallyaffect product performance. Temperate humid was also characterized ashaving a temperature and solar radiation lower than that identified inthe temperate environmental class and found to be frequent in Indiana,Ohio, and Pennsylvania. The High Latitude environmental class was foundto grow corn CRM 103 and earlier (growing hybrids) and experiencedcolder temperatures than the Temperate environmental class but withlonger day-length. This environmental class was found to be frequent inCanada, North Dakota, Minnesota, Michigan, and Wisconsin. The fifthenvironmental class, Subtropical, was characterized as warm and humidwith a short day-length and found frequently in the Deep South of theUnited States. Another environmental class identified was European CornBorers (ECB) and defined as having Bacillus thuringiensis (Bt) hybridsthat outyielded base genetics by at least 10%. The last environmentalclass Brittle defined areas with significant brittle damage withdifferential effect on products.

Once areas of land are categorized as environmental classes, these areasmay be used in METs. Ultimately, the observed genotype performances inMETs can be linked by the environmental class to the TPE. By evaluatingproduct performance in a target environment, rather than merelyperformance differences in METs, genotype performance data from multipletest environments can be correlated to a target environment and used topredict product performance. This correlation between a genotype'sperformance and the target environment or environmental classificationwill lead to more precise product placement since the genotypeperformance is characterized within an environmental class in which itis adapted and most likely to experience after commercialization,consequently resulting in improved and more predictable productperformance. The analysis of G×E interactions facilitates the selectionand adoption of genotypes that have positive interactions with itslocation and its prevailing environmental conditions (exploitation ofareas of specific adaption). G×E analysis also aids in theidentification of genotypes with low frequency of poor yield or otherperformance issues in certain environments. Therefore, G×E analysis willhelp in understanding the type and size of G×E interactions expected ina given region. The present inventors contemplate that proper selectionof hybrids for a particular land base will improve agriculturalpotential of certain geographic areas by maximizing the occurrence ofcrop performance through the use of the environmental classification. Inaddition, this approach allows the use of statistical and probabilitybased analysis to quantify the risk of product success/failure accordingto the frequency of environment classes and the relative performance ofgenotypes within each environment class. This early identification andselection of hybrids would enable seed producers to start seedproduction and accelerate the development of hybrids in winter nurseriesin warmer southern climates.

Moreover, environmental classification allows for the creation of anenvironmental profile for all or any part of the land base classified.Environmental classifications can be determined for each producer's landbase. Similarly, the environmental performance profile ofcultivars/hybrids can be determined through field experimentation orpredicted using G×E analysis. In combining environmental classificationfrequencies for a particular land base and product performance byenvironmental classification, performance measurements are given theappropriate amount of relevance or weight for the land base in question.For example, the data are weighted based on long-term frequencies tocompute a prediction of hybrid performance.

Use of G×E in Producer's Selection

According to another aspect of the present invention, a method of usinginformation that documents the environmental profile over time of a cropproducer's land base, the environmental performance profile of cropcultivars, and the producer's objectives to select a portfolio ofcultivars that maximizes and quantifies the probability that theproducer's objectives for productivity will be met. Environmentalclassification can be used to assist in this process.

Environmental classification can be used to determine the primaryenvironmental drivers of G×E interaction in crops such as corn. That is,what are the primary environmental factors that cause change in therelative performance of hybrids. With this knowledge, crop productionareas can be categorized into environmental frequency classes. Withinthese classes, hybrids tend to perform (as measured by yield) relativelysimilar to one another. Across these classes, the relative performanceof hybrids tends to be significantly different. Using historicalmeteorological information along with soils, pests, and other agronomicinformation, the frequency of these environments can be determined. Thisallows the creation of an environmental profile for all or any part ofthe geography classified. That is, a frequency distribution of theoccurrence of the key Environment Classes. This can be done for eachcrop producer's land base.

Similarly, the environmental performance profile of crop cultivars canbe determined through field experimentation. That is, a description ofrelative performance of cultivars can be determined in each of the keyenvironment classes. In combining Environment class frequencies for aparticular land area and product performance by Environment Class,performance measurements are given an appropriate amount of relevance orweight for the land area in question

Thus, this aspect of the invention involves combining of thisinformation at the producer's level to optimize crop productivity insuch a way that it maximizes the probability of the producer's businessoperation reaching its productivity goals. The present inventioncontemplates that information can be used from any number ofclassification schemes to the selection of cultivars with the objectiveof maximizing the probability of attainment of the productivity andbusiness goals of a crop producer's operation.

The approach of this aspect of the present invention does so by usingcompiled long term geo-referenced weather, soils, and agronomic dataincluding biotic factors for the producer's land base to categorize theland base in terms of how frequently annual environmental variationoccurs to a degree that is likely to impact relative hybrid performance.In addition, it can incorporate the producer's business objectivesincluding, but not limited to preparedness to take risk. The presentinvention is able to combine environmental variability with producerbusiness information to create a producer profile. Product performanceinformation stratified by the same criteria is used to define theproducer's environmental profile (for example, environmental classes)which is then integrated with the producer's profile.

The relative hybrid performance information that is relevant to theproducer's land base is used regardless of when and where it wasgenerated. The present inventors are first to predict future performanceof genotypes and quantify probability/risk associated with thatperformance using data from environments that are considered to besubstantially equivalent in terms of relative hybrid response. Theresult is a more robust and predictive data set thus allowing moreinformed product selection decisions that, over time will result in ahigher probability of a producer operation meeting business objectivesfor productivity.

FIG. 8 illustrates information flow according to one embodiment of thepresent invention. In FIG. 8 there is an environmental profile 100. Theenvironmental profile can be based on one or more inputs such asenvironment classes 102, meteorological information 104, agronomicinformation 106, or field experimentation 108. In FIG. 1 there is also aproducer profile 110. The producer profile 110 is based on one or moreinputs such as risk tolerance 112 of the producer, business goals 114 ofthe producer, productivity goals 116, financing 118 considerations,third party needs 119, for example a landlord, or insurance/riskmanagement and marketing 120 considerations. The environmental profile100 and the producer profile 110 are combined in order to producerecommendations 122. The recommendations 122 can include risk managementtools, a recommended seed product, a recommended mix of seed products,production practice recommendations, such as chemical applicationinformation, or any number of other specific recommendations as may beappropriate based on the particular environmental profile 100 andproducer profile 110.

FIG. 9 illustrates one embodiment of a system 124 for producing productrecommendations. In FIG. 9, a processor 126 accesses informationassociated with a producer profile 110, an environmental profile 100,and a genotype by environment database 132. There is an input device128, a recommendation output 129, and a display 130 operativelyconnected to the processor. The present invention contemplates that theprocessor 126 can be associated with a computer such as handheldcomputer as may be convenient for a dealer or sales agent. The presentinvention also contemplates that the producer profile 110, environmentalprofile 100, and genotype by environment database 132 may be accessibleover a network, including a wide-area network such as the Internet.

Using the information in the producer profile 110, environmental profile100, and genotype-by-environment database 132, the processor applies oneor more of a product selection algorithm module 134, a productcomparator 136, a production practice module and a risk comparator 138,and a product portfolio module 140. These and/or other modules arecollectively the recommendation logic 142. In a simple case, the productselection algorithm module 134 would take information from theenvironmental profile 100, such as an environmental classification(“Temperate”, for example) in addition to information from the producerprofile 110, such as a producer objective (“Maximize Yield”, “RiskMinimization”, “Low Harvest Moisture” for example) and match thesecriteria to products in the genotype-by-environment database 132. Ofcourse, more specific criteria could be examined as would be the casewith more complex environmental profile information and more complexproducer profile information.

FIG. 10 illustrates one embodiment of a screen display 144 of a softwareapplication the present invention. In FIG. 10, a user is given thechoice of selecting “DEFINE ENVIRONMENTAL PROFILE” 146, “DEFINE PRODUCERPROFILE” 148, and “VIEW RECOMMENDATIONS” 150. Of course, the presentinvention contemplates that software and its accompanying user interfacecan be implemented in any number of ways.

FIG. 11 illustrates one embodiment of a screen display 152 of a softwareapplication of the present invention. In FIG. 11, a recommendation isgiven which includes a plurality of products 154, an associated numberof acres 156 associated with each of the products, a risk/probabilityassessment 157, and a recommended crop revenue assurance 158. Thepresent invention provides for decreasing the amount of risk associatedwith selection of a particular seed product by instead selectingmultiple products with different G×E interactions in order to reducerisk associated with environmental variations. The resulting selection,is somewhat akin to selection of stocks in a stock portfolio.

FIG. 12 and FIG. 13 illustrate embodiments of user interfaces to use inprecision farming applications. In FIG. 12, the user interface 170includes site-specific information associated with location information172. The present invention contemplates that other site-specificinformation or historical information is accessible based on thelocation information 172 and may be used in product selections. Inaddition, environment and production information is collected. Examplesof such information includes maturity days 176, input traits 178, outputtraits 180, seed treatment 182, whether no till practices 174 are used,the planting population 184, nitrogen utilization 186, and droughtimpact based on environmental classification drought frequencyinformation 187 and soil type. Based on this information and informationassociated with the location 172, a recommendation 188 of at least onehybrid seed product is made. Where multiple recommendations are made,the recommendations can be ranked as well as a risk assessment 189 suchas shown.

FIG. 13 illustrates another embodiment of a user interface 200 that canbe used in crop production applications. Site specific information iscollected such as location 172, soil type 174, and number of acres 202.In addition, there is the option to import precision farming data 204 aswell as import environment of frequency data 205. There are also theoptions to set production practices, set environmental assumptions, setrisk levels, and set the maximum number of hybrids 212. Based on theinputs, a portfolio is created that includes a plurality of products214, an associated number of acres 216 to plant for each product, arecommendation 217 of at least one hybrid seed product, a riskassessment 218, and revenue assurance 219. Where multiplerecommendations are made, the recommendations can be ranked. There isalso an option to generate precision farming information 220 based onthis information, such as a prescription map. The present inventioncontemplates that the precision farming information may indicate whichacres to plant with which hybrids, give specific production practiceapplication (such as chemical application rates), or otherrecommendations.

FIG. 14 illustrates one example of a field-by-field analysis showingproduct recommendations for a land base of a producer. As shown in FIG.14, different land areas within a producer's land base have differenthybrids associated with them. The present invention contemplatesproducing such a map or field-by-field recommendations where multipleproducts are recommended. It should further be understood that a singleproducer or other user may have operations in a number of geographicallydiverse locations, and not necessarily the nearby fields illustrated inFIG. 14.

It should also be appreciated that the use of environmentalclassification and G×E interactions should be effectively communicatedto customers. The effectiveness of the environmental classificationprocess is based in part on its ability to use historical data from manylocations so that all available data is used. This aspect ofenvironmental classification would seem counter-intuitive to a customerwho primarily relies upon personal knowledge in the local area. Thecustomer's confidence in firsthand production knowledge can be used toassist in increasing confidence in environmental classification.

FIG. 15 illustrates one example of the methodology of this aspect of theinvention to assist in explaining these concepts to a producer. In step300 site-specific data collection for a land base is performed. Based onthis site-specific data collection, in step 302, the land base is givenan environmental classification. In addition to this information, thetype of hybrid selected in the previous year and its performance isprovided by the producer in step 304. In step 306, a prediction is madeas to the previous year's production based on environmentalclassification. In step 308, the predicted results are compared with theactual results. The present invention also contemplates not requiringperformance results from the producer until after the previous year'sresults have been predicted in case the producer is not confident thatan independent prediction is made.

FIG. 16 illustrates one example of a screen display showing suchcomparisons. In FIG. 16, performance predictions (yield) are made for anumber of different hybrids for both the previous year and the currentyear. In addition, a risk assessment for each hybrid may also beprovided. The producer can compare the prediction for the previous yearwith the actual performance for that year in order to understand howwell the environmental classification method can predict a result. Ifthe producer is confident in the method's ability to correctly predict aresult, the producer will be more inclined to use the prediction madefor the coming year. The present invention contemplates that the same orsimilar information can be presented in any number of ways. It shouldfurther be understood that such a demonstrate assists in illustratingthe accuracy of the system in predicting relative performancedifferences between seed products. Due to the number of potentialvariables and difficulty in controlling such variable accurateprediction of absolute performance is generally not a reasonable goal.However by selecting appropriate environmental classifications, usefulinsight into relative performance can be provided.

Compensation and Evaluating the Land Base

The present invention recognizes that agricultural input suppliersbenefit from the success which they assist crop producers in obtaining.For example, when a seed product performs exceptionally well for aproducer, such a seed product may be perceived as being of higherquality than competing products in future years. When a seed productperforms poorly, such as seed product may be perceived as being of alower quality or undesirable and the producer and other producers may bedisinclined to purchase the seed product in future years. It should beappreciated that these perceptions are not facts, but merely one datapoint. While the genotype for each of the products may be capable ofproducing high performers, the circumstances regarding the environment,and the resulting G×E interactions may have limited performance.Therefore, the result of the performance has very limited utility whenviewed in isolation because the same or highly similar environmentalconditions may not be present in the future years. The use of theenvironmental classification system of the present invention isadvantageous as it incorporates significant data and therefore does notlimit one to an isolated and restrictive view of the performance of anagricultural input.

As previously indicated, there may be some resistance to use of anenvironmental classification system by particular producers because itrequires reliance on data that was not observed firsthand. Also, aspreviously indicated there is a benefit to suppliers of agriculturalinputs to have producers provide the best results. To increase thelikelihood of those results the present invention, as an example,provides for tying the compensation received for the seed product toevaluating the land base on which the seed is to be planted and/or theperformance of the seed product on the land base. One way of evaluatingthe land base is by promoting the use of environmental classification orother systems that take into account G×E interactions.

The compensation received for the seed products can be determined in avariety of different ways and can be of many different types. This caninclude, without limitation, pricing the seed product based on theperformance of the seed product in the land base, determining thecompensation received based at least partially on the quality of theland base, providing the at least one seed products in exchange for thecompensation under a license agreement, and/or tying the use of the atleast one seed product within the land base to a cost of use of the atleast one seed product.

FIG. 18 illustrates one embodiment of the present invention where anagricultural input supplier 500 provides a seed product 502 to aproducer 504 in exchange for compensation under a license agreement 506.The license agreement 506 includes an evaluation of the land base 514based on an environmental classification 508 of the land base 514, adetermination of the location 510 of the land base 514, the seed product502 to be used at the land base 514, and the price 512 of the seedproduct 502. As a means of auditing the performance of the seed product502 at the land base 514, verification means 516 are employed. These mayinclude remote sensing of the field, GPS data associated with cropproduction, weigh tickets, and yield monitoring data. Note that theverification can be of various types, including verifying that theproper type and amount of seed is planted at the agreed upon location aswell as verifying the performance of the seed product.

Based on several factors, including but not limited to the environmentalclassification and performance of the genotype of each of the at leastone seed product in the environmental profile of the land base, arecommendation for a producer regarding which seed products to use inthe land base can be made. The producer accepting the recommendation andmaking purchases based on the recommendation may be a condition ofreceiving the seed product in exchange for compensation which is tied tothe evaluation of the land base. The recommendation may include theselection of one or more specific products, or may include arecommendation that one or more products be selected from a particularset of products. Such a methodology encourages the producer in makingdecisions based on G×E interactions and/or environmental classification.

By tying the compensation received for seed products based on anevaluation of the land base and/or the performance of the seed products,the agricultural input supplier is picking up some of the risk ofagricultural production. Alternating their role may be expressed asparticipating in value creation. One way the agricultural input suppliercan add to the value is by making specific recommendations of inputs orpractices which will or likely will enhance the value or performance ofthe crop. By making good recommendations, the agricultural supplierreduces their risks.

The agricultural input supplier can further offset the additional riskby taking positions in the grain market. In one embodiment of theinvention, this may include purchasing options in order to cover anypotential losses as a result of tying the compensation received for theseed products to evaluating the land base and/or the performance of theseed products on the land base. In other words, the agriculturalsupplier risks loss of income if its customers' crops perform poorly.When the customers' crops perform poorly if there is poor performancegenerally, then there is a greater likelihood that the price of cropswill rise. Thus, the input supplier can manage risks by takingappropriate market positions. The agricultural input supplier canfurther manage risks by tying performance to price in diversegeographical locations and over a range of agricultural inputs tofurther diversify.

An additional way for the agricultural input supplier to offset risk isto require the producer to make a minimum payment for the seed. Theagricultural input supplier might also require the use of riskmanagement instruments, such as crop insurance or crop revenue insurancebased on environmental classification of the land base and therecommendations and risk assessments for seed products, herbicides,insecticides, and other inputs or production practices. Of course, thepresent invention contemplates combining this information with otherinformation that may be used in determining the compensation to bereceived in exchange for the seed products. The input supplier mayrequire insurance, such as multi-peril or catastrophic coverage, toensure the producer would be able to make the minimum payment for theseed. Crop revenue coverage may also be required to protect against lostrevenue caused by low prices, low yields or any combination of the two.

Another type of risk confronting an input supplier is if a producer doesnot comply with the terms of the agreement. For example, a producer maytry to buy seed for one location but plant it at another location. Aproducer may try to over buy seed for a location where the purchaseprice is less and plant some of the seed elsewhere. A producer mayunder-report past or actual performance. Thus, the input supplier needsto manage the risk associated with these and other scenarios.

In order to minimize this risk, the present invention provides for meansfor verifying the performance of the seed product in the land base. Theverification may include the use of GPS data associated with plantingand/or harvesting the seed products at the land base, reviewing weightickets and/or yield monitoring data for crops produced from the landbase, and/or remote sensing of the land base. The GPS data can be usedto verify that planting operations occurred at a particular location andother planting details that assist in verifying this information. Ofcourse, in addition to or instead of analyzing this data, field serviceagronomists and/or crop scouts can also verify information.

FIG. 19 provides an example of auditing the performance of a seedproduct. In FIG. 19, a license is formed which includes a determinationof the location of the land base 550, a classification of the land base552 and a recommendation of seed products for use on the land base 554.The present invention provides for using environmental classificationand product recommendations in determining the licensing terms andconditions. The performance of the seed product may be audited at theplanting stage by reviewing GPS data associated with planting the seedproduct 556. At the growing stage, the seed product may be audited byuse of remote sensing 558. At the harvest stage, reviewing GPS dataassociated with harvesting the seed products 560, reviewing weightickets associated with crop production 562, and/or reviewing yieldmonitoring data 564 may be used to audit the performance of the seedproducts at the land base.

The present invention contemplates numerous variations from the specificembodiments provided herein. These include variations in theenvironmental classifications, performance characteristics, software orhardware where used, the type of and other variations.

All publications, patents and patent applications mentioned in thespecification are indicative of the level of those skilled in the art towhich this invention pertains. All such publications, patents and patentapplications are incorporated by reference herein for the purpose citedto the same extent as if each was specifically and individuallyindicated to be incorporated by reference herein.

1. A method of selling seed products for planting by a crop producercomprising: characterizing a land base at which the seed will beplanted; determining the seed to be planted at the land base pricing theseed product based on performance of the seed product within the landbase.
 2. The method of claim 1 wherein the step of characterizing theland base comprises providing an environmental classification of theland base.
 3. The method of claim 1 wherein the performance is expectedperformance.
 4. The method of claim 3 wherein the expected performanceis at least partially based on characteristics of the land base.
 5. Themethod of claim 4 wherein the characteristics of the land base are basedat least partially on an environmental classification associated withthe land base.
 6. The method of claim 1 wherein the step of determiningthe seed to be planted at the land base is at least partially based onperformance of the seed product associated with an environmentalclassification.
 7. The method of claim 1 wherein the performance isactual performance.
 8. The method of claim 1 further comprisingrecommending at least one type of seed product for use in the land base.9. The method of claim 8 where the step of recommending at least onetype of seed product for use in the land base is at least partiallybased on genotype-by-environment interactions between the seed productand the land base.
 10. The method of claim 9 wherein thegenotype-by-environment interactions are determined at least partiallybased on performance data associated with the seed products.
 11. Themethod of claim 9 wherein the genotype-by-environment interactions aredetermined at least partially based on environmental classificationsassociated with performance data of the seed products.
 12. The method ofclaim 1 further comprising auditing the performance of the at least oneseed product within the land base.
 13. The method of claim 1 wherein thestep of pricing the seed product based on performance of the seedproduct within the land base is performed by a computer in operativecommunication with a database of performance data associated with landbases.
 14. A method of selling seed products to a producer whereincompensation for the seed products is tied to the quality of the landbase of the producer, the method, comprising: evaluating the land baseof the producer to determine a quality of the land base; determiningcompensation for at least one seed product at least partially based onthe quality of the land base; and providing the at least one seedproducts to the producer in exchange for the compensation.
 15. Themethod of claim 14 wherein the step of evaluating the land basecomprises providing an environmental classification of the land base.16. The method of claim 15 wherein the environmental classification isselected from a set of environmental classes, the set of environmentalclasses comprising a temperate class, a temperate dry class, a temperatehumid class, a high latitude class, and a subtropical class.
 17. Themethod of claim 15 wherein the environmental classification is selectedfrom a set of environmental classes, the set of environmental classescomprising biotic classifications.
 18. The method of claim 15 whereinthe quality of the land base is associated with the environmentalclassification.
 19. The method of claim 15 wherein the quality of theland base is associated with performance of the seed product associatedwith the environmental classification.
 20. The method of claim 14further comprising recommending at least one type of seed product foruse in the land base.
 21. The method of claim 20 further comprisingselecting crop insurance based on the recommendation.
 22. The method ofclaim 20 where the step of recommending at least one type of seedproduct for use in the land base is at least partially based ongenotype-by-environment interactions between the at least one seedproduct and the land base.
 23. The method of claim 22 wherein thegenotype-by-environment interactions are determined at least partiallybased on performance data associated with the seed products.
 24. Themethod of claim 23 wherein the genotype-by-environment interactions aredetermined by statistical methods.
 25. The method of claim 22 where thegenotype-by-environment interactions are determined by a qualitativemethod.
 26. The method of claim 25 where the qualitative method isphenotype plasticity.
 27. The method of claim 22 wherein thegenotype-by-environment interactions are determined at least partiallybased on environmental classifications associated with performance dataof the seed products.
 28. The method of claim 27, wherein saidperformance data includes at least one item from the set consisting ofyield, drought resistance, grain moisture, lodging, stand establishment,emergence, midsilk, test weight, protein, oil, and starch percentage,relative maturity, plant height, seed size, disease resistance genes,heading date, resistance to insects, brittle snap, stalk breakage,resistance to fungus, seed moisture, head shape, hullability, seedlingvigor, beginning bloom date, maturity date, seed shatter, wintersurvival, fiber strength, and color grade.
 29. The method of claim 14wherein the step of determining compensation for at least one seedproduct is at least partially based on both the quality of the land baseand the expected performance of the at least one seed product within theland base.
 30. The method of claim 14 wherein the step of determiningcompensation for at least one seed product is at least partially basedon both the quality of the land base and the actual performance of theat least one seed product within the land base.
 31. The method of claim14 further comprising auditing the performance of the at least one seedproduct within the land base.
 32. The method of claim 31 wherein thestep of auditing incudes auditing GPS data associated with cropproduction in the land base.
 33. The method of claim 32 wherein the GPSdata is associated with planting the at least one seed product in theland base.
 34. The method of claim 32 wherein the GPS data is associatedwith harvesting the at least one seed product from the land base. 35.The method of claim 31 wherein the step of auditing includes reviewingweigh tickets for crops produced from the land base.
 36. The method ofclaim 31 wherein the step of auditing includes reviewing yieldmonitoring data for crops produced from the land base.
 37. The method ofclaim 31 wherein the step of auditing comprises remote sensing of theland base.
 38. The method of claim 14 wherein the step of providing theat least one seed products in exchange for the compensation furthercomprises providing the at least one seed products in exchange for thecompensation under a license agreement.
 39. The method of claim 14wherein the at least one seed product is a plurality of seeds.
 40. Themethod of claims 14 wherein the step of determining compensation for atleast one seed product at least partially based on the quality of theland base is performed by a computer in operative communication with adatabase of performance data associated with land bases.
 41. A methodfor auditing performance of at least one seed product used in a landbase in a predetermined location, comprising: determining the locationof the land base at which the at least one seed product will be used;tying the use of the at least one seed product within the land base to acost of use of the at least one seed product; and verifying use of theat least one seed product within said land base.
 42. The method of claim41 further comprising verifying performance of the seed product withinthe field.
 43. The method of claim 41 wherein the step of verifyingincudes auditing GPS data associated with crop production in the landbase.
 44. The method of claim 43 wherein the GPS data is associated withplanting the at least one seed product in the land base.
 45. The methodof claim 43 wherein the GPS data is associated with harvesting the atleast one seed product from the land base.
 46. The method of claim 41wherein the step of verifying includes reviewing weigh tickets for cropsproduced from the land base.
 47. The method of claim 41 wherein the stepof verifying includes reviewing yield monitoring data for crops producedfrom the land base.
 48. The method of claim 41 wherein the step ofverifying comprises remote sensing of the land base.
 49. The method ofclaim 41 further comprising providing an environmental classification ofthe land base.
 50. The method of claim 49 wherein the cost of use of theat least one seed product is at least partially determined based onexpected genotype-by-environment interactions between the environmentalclassification associated with the land base and the at least one seedproduct.
 51. The method of claim 41 further comprising recommending atleast one type of seed product for use in the land base.
 52. The methodof claim 41 further comprising providing the at least one seed productin exchange for compensation, wherein said compensation is the cost ofuse of the at least one seed product.
 53. A method for auditingperformance of at least one seed product used in land base in apredetermined location using GPS data associated with crop production inthe land base, the method comprising: determining the location of theland base at which the at least one seed product will be planted;providing the at least one seed product to be used at the land base inexchange for a compensation; tying the use of the at least one seedproduct within the land base to the compensation; and verifying the useof the at least one seed product using GPS data associated with cropproduction in the land base.
 54. The method of claim 53 wherein the GPSdata is associated with planting the at least one seed product in theland base.
 55. The method of claim 53 wherein the GPS data is associatedwith harvesting the at least one seed product from the land base. 56.The method of claim 53 further comprising verifying performance of theseed product within the field.
 57. The method of claim 53 furthercomprising providing an environmental classification of the land base.58. The method of claim 57 wherein the cost of use of the at least oneseed product is at least partially determined based on expectedgenotype-by-environment interactions between the environmentalclassification associated with the land base and the at least one seedproduct.
 59. The method of claim 53 further comprising recommending atleast one type of seed product for use in the land base.
 60. A method oflicensing at least one seed product for planting by a crop producerassociated with a land base comprising a plurality of fields, the methodcomprising: determining the at least one seed product to be planted ineach of the plurality of fields of the land base; providing anenvironmental classification of each field within the land base; anddetermining a cost of the at least one seed product for each fieldwithin the land base at least partially based on the classification ofeach field within the land base.
 61. The method of claim 60 furthercomprising recommending at least one type of seed product for use ineach of the plurality of fields of the land base.
 62. The method ofclaim 61 where the step of recommending at least one type of seedproduct for use in the land base is at least partially based ongenotype-by-environment interactions between the at least one seedproduct and each of the plurality of fields of the land base.
 63. Themethod of claim 62 wherein the genotype-by-environment interactions aredetermined at least partially based on performance data associated withthe seed products.
 64. The method of claim 62 wherein thegenotype-by-environment interactions are determined at least partiallybased on environmental classifications associated with performance dataof the seed products.
 65. The method of claim 60 wherein the step ofdetermining a cost of the at least one seed product is at leastpartially based on both the characterization of each of the plurality offields within the land base and the expected performance of the at leastone seed product within each of the plurality of fields within the landbase.
 66. The method of claim 60 wherein the step of determining a costof the at least one seed product is at least partially based on both thecharacterization of each of the plurality of fields within the land baseand the actual performance of the at least one seed product within eachof the plurality of fields within the land base.
 67. The method of claim60 further comprising auditing the performance of the at least one seedproduct within each of the plurality of fields within the land base. 68.A method of recommending at least one seed product for planting by acrop producer associated with a land base comprising a plurality offields, the method comprising: identifying the location of the land baseat which the at least one seed product will be planted; classifying theland base to provide an environmental classification; determining arecommendation of the at least one seed product to be used at the landbase based on the environmental classification and performance of thegenotype of each of the at least one seed product in the environmentalprofile of the land base; determining compensation for the at least oneseed product at least partially based on the quality of the land base;and providing the at least one seed products to the producer in exchangefor the compensation.
 69. The method of claim 68 wherein the step ofdetermining a cost of the at least one seed product is at leastpartially based on both the characterization of each of the plurality offields within the land base and the expected performance of the at leastone seed product within each of the plurality of fields within the landbase.
 70. The method of claim 68 wherein the step of determining a costof the at least one seed product is at least partially based on both thecharacterization of each of the plurality of fields within the land baseand the actual performance of the at least one seed product within eachof the plurality of fields within the land base.
 71. The method of claim68 further comprising auditing the performance of the at least one seedproduct within each of the plurality of fields within the land base.